Evidence map›Paper›PMID 37908765›Full record

ArticleNeuro-oncology advances

MRI phenotypes of glioblastomas early after treatment are suggestive of overall patient survival.

Bárbara Schmitz-Abecassis, Linda Dirven, Janey Jiang, Jasmin A Keller, Robert J I Croese, Daniëlle van Dorth, Rashid Ghaznawi, Ilse M J Kant, Martin J B Taphoorn, Matthias J P van Osch and 2 more

Abstract read
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Article in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Bárbara Schmitz-AbecassisC.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.ORCID https://orcid.org/0000-0003-1776-4560
Linda DirvenDepartment of Neurology, Leiden University Medical Center, Leiden, The Netherlands.ORCID https://orcid.org/0000-0001-9157-9895
Janey JiangDepartment of Radiology, HagaZiekenhuis, The Hague, The Netherlands.
Jasmin A KellerC.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.ORCID https://orcid.org/0000-0001-7075-7525
Robert J I CroeseDepartment of Neurology, Leiden University Medical Center, Leiden, The Netherlands.ORCID https://orcid.org/0000-0003-2378-4525
Daniëlle van DorthC.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.ORCID https://orcid.org/0000-0001-7535-8072
Rashid GhaznawiDepartment of Neurology, Leiden University Medical Center, Leiden, The Netherlands.ORCID https://orcid.org/0000-0002-6616-5276
Ilse M J KantClinical Artificial Intelligence Implementation and Research Lab (CAIRELab) and Department of Information Technology & Digital Innovation, Leiden University Medical Center, Leiden, The Netherlands.ORCID https://orcid.org/0000-0002-5273-5178
Martin J B TaphoornC.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.ORCID https://orcid.org/0000-0001-9949-4722
Matthias J P van OschMedical Delta, South-Holland, The Netherlands.ORCID https://orcid.org/0000-0001-7034-8959
Johan A F KoekkoekDepartment of Neurology, Leiden University Medical Center, Leiden, The Netherlands.ORCID https://orcid.org/0000-0002-3648-9616
Jeroen de BresserC.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.ORCID https://orcid.org/0000-0003-0759-8407

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Distinguishing true tumor progression (TP) from treatment-induced abnormalities (eg, pseudo-progression (PP) after radiotherapy) on conventional MRI scans remains challenging in patients with a glioblastoma. We aimed to establish brain MRI phenotypes of glioblastomas early after treatment by combined analysis of structural and perfusion tumor characteristics and assessed the relation with recurrence rate and overall survival time. Methods: Structural and perfusion MR images of 67 patients at 3 months post-radiotherapy were visually scored by a neuroradiologist. In total 23 parameters were predefined and used for hierarchical clustering analysis. Progression status was assessed based on the clinical course of each patient 9 months after radiotherapy (or latest available). Multivariable Cox regression models were used to determine the association between the phenotypes, recurrence rate, and overall survival. Results: We established 4 subgroups with significantly different tumor MRI characteristics, representing distinct MRI phenotypes of glioblastomas: TP and PP rates did not differ significantly between subgroups. Regression analysis showed that patients in subgroup 1 (characterized by having mostly small and ellipsoid nodular enhancing lesions with some hyper-perfusion) had a significant association with increased mortality at 9 months (HR: 2.6 (CI: 1.1-6.3); Conclusions: Our study suggests that distinct MRI phenotypes of glioblastomas at 3 months post-radiotherapy can be indicative of overall survival, but does not aid in differentiating TP from PP. The early prognostic information our method provides might in the future be informative for prognostication of glioblastoma patients.

Indexed as

glioblastomaMRIperfusiontumor progression

Identifiers

PMID37908765
PMCPMC10613962

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.